The sustained escalation in aero-engine thrust-to-weight ratios and turbine inlet temperatures has established thermal barrier coatings (TBCs) as an indispensable solution for surpassing the intrinsic temperature limits of hot-section components and ensuring their long-term service reliability. Nevertheless, the service response of TBCs involves exceedingly complex thermo-mechanical-chemical (TMC) multi-field coupling and cross-scale damage evolution, which poses severe fundamental challenges to conventional single-scale experimental and analytical methodologies. This review systematically summarizes the advances in conventional and advanced modeling, simulation, and analysis techniques for TBC and further extends the discussion to the application of artificial intelligence (AI) technologies. The study aims to provide a systematic overview of analysis methods for TBC service behavior and structural optimization strategies for coatings and to promote the implementation of proactive maintenance strategies. The technical methods covered include thermal conductivity modeling, thermal stress simulation, crack propagation analysis, and mechanical behavior studies of TBC systems, which predominantly rely on diverse techniques such as multiscale modeling and finite element analysis. The paper focuses on the use of AI in TBC simulation, including data-driven modeling approaches and machine learning algorithms such as support vector machines, random forests, neural networks, and deep learning. The findings indicate that AI holds the potential to revolutionize TBC technology in multiple respects, encompassing material and structural design, performance optimization and prediction, condition monitoring, fault diagnosis and health management, and remaining useful life (RUL) prediction, while significantly improving prediction accuracy. Novel TBCs are anticipated to offer robust support for next-generation aero-engines to transcend temperature limits and achieve improved efficiency and reliability with ongoing advancements in the intelligent design of new materials.

Calcium–magnesium–alumina–silicate (CMAS) corrosion has attracted special attention in the thermal barrier coating (TBC) field. At high temperatures, when CMAS melts, it adheres to the coating surface and penetrates the interior, severely destroying the TBC. In this study, a promising CMAS-phobic and infiltration-inhibiting material, GdPO₄, on which molten CMAS is difficult to wet and penetrate, was proposed. These desirable attributes are explained by analyzing the material characteristics of GdPO₄ and its interfacial reaction with CMAS. GdPO₄ is demonstrated to have low surface energy, making it difficult for molten CMAS to wet and adhere to the surface. When in contact with molten CMAS, a double-layer structured reaction layer consisting of an acicular upper sublayer and a compact lower sublayer is formed on the GdPO₄ surface, which can effectively impede molten CMAS spreading and penetration. First-principles calculation results revealed that the reaction layer has low surface energy and low adhesion to CMAS, which are favorable for molten CMAS phobicity. Additionally, the formation of the reaction layer increases the viscosity of the molten CMAS, which can increase melt wetting and penetration. Hence, GdPO₄, which exhibits excellent CMAS-phobicity and infiltration-inhibiting ability, is a promising protective layer material for TBCs against CMAS adhesion and attack.